Modeling and Multivariate Methods - SAS

Chapter 18 Clustering Data 469
K-Means Clustering
K-Means Clustering
The k-means approach to clustering performs an iterative alternating fitting process to form the number of
specified clusters. The k-means method first selects a set of n points called cluster seeds as a first guess of the
means of the clusters. Each observation is assigned to the nearest seed to form a set of temporary clusters.
The seeds are then replaced by the cluster means, the points are reassigned, and the process continues until
no further changes occur in the clusters. When the clustering process is finished, you see tables showing
brief summaries of the clusters. The k-means approach is a special case of a general approach called the EM
algorithm; E stands for Expectation (the cluster means in this case), and M stands for maximization, which
means assigning points to closest clusters in this case.
The k-means method is intended for use with larger data tables, from approximately 200 to 100,000
observations. With smaller data tables, the results can be highly sensitive to the order of the observations in
the data table.
K-Means clustering only supports numeric columns. K-Means clustering ignores model types (nominal and
ordinal), and treat all numeric columns as continuous columns.
To see the KMeans cluster launch dialog (see Figure 18.4), select KMeans from the Options menu on the
platform launch dialog. The figure uses the Cytometry.jmp data table.
Figure 18.4 KMeans Launch Dialog
The dialog has the following options:
Columns Scaled Individually is used when variables do not share a common measurement scale, and
you do not want one variable to dominate the clustering process. For example, one variable might have
values that are between 0-1000, and another variable might have values between 0-10. In this situation,
you can use the option so the clustering process is not dominated by the first variable.
Johnson Transform
the values.
balances highly skewed variables or brings outliers closer to the center of the rest of